Preprint2023
LongLoRA
Unknown
LongLoRA enables efficient context extension for large language models via sparse local attention and parameter-efficient fine-tuning.
0Sep 1, 2023Large Language ModelsAttention Mechanisms
AI Research
AI & ML Research Papers
A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
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12 papersPreprint2023
DyLoRA: Parameter-efficient tuning of pre-trained models using dynamic search-free low-rank adaptation
Unknown
DyLoRA introduces a dynamic, search-free low-rank adaptation method that trains LoRA blocks for a range of ranks to improve parameter-efficient tuning of pre-trained models.
0Jan 1, 2023
ConferenceAnnual Meeting of the Association for Computational Linguistics2023
Parameter-efficient fine-tuning without introducing new latency
Baohao Liao, Yan Meng, C. Monz
This paper proposes a parameter-efficient fine-tuning method that matches full fine-tuning performance without introducing additional inference latency.
84Jan 1, 2023Fine Tuning
arXiv
Preprint2024
Parameter-efficient fine-tuning with discrete fourier transform
Ziqi Gao, Qichao Wang, Aochuan Chen, et al.
FourierFT is a parameter-efficient fine-tuning method that learns weight updates in the frequency domain via discrete Fourier transform.
0Jan 1, 2024Fine Tuning
arXiv
Preprint2024
Parameter-efficient fine-tuning for pre-trained vision models: A survey and benchmark
Yi Xin, JianJiang Yang, Siqi Luo, et al.
A survey and benchmark of parameter-efficient fine-tuning methods for pre-trained vision models.
0Jan 1, 2024Fine TuningBenchmarks
arXiv
Preprint2025
Parameter-efficient fine-tuning in large language models: a survey of methodologies
Unknown
This survey provides a comprehensive overview of parameter-efficient fine-tuning (PEFT) methodologies for large language models, addressing the computational challenges of full fine-tuning.
0Jan 1, 2025Large Language ModelsFine Tuning
Preprint2022
Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning
Unknown
Parameter-efficient fine-tuning (PEFT) achieves better performance and lower cost than in-context learning for few-shot tasks.
0Jan 1, 2022Fine TuningFew Shot Learning
Preprint2026
Parameter-efficient fine-tuning methods for pretrained language models: A critical review and assessment
Unknown
This paper critically reviews and assesses parameter-efficient fine-tuning methods for pretrained language models, highlighting their ability to reduce parameters and memory while maintaining performance.
0Jan 1, 2026Fine Tuning
ConferenceNature Machine Intelligence2023
Parameter-efficient fine-tuning of large-scale pre-trained language models
Ning Ding, Yujia Qin, Guang Yang, et al.
This paper surveys parameter-efficient fine-tuning methods for large pre-trained language models, categorizing approaches and analyzing their trade-offs.
1.2kJan 1, 2023Fine Tuning
Preprint2023
Scaling down to scale up: A guide to parameter-efficient fine-tuning
V. Lialin, Vijeta Deshpande, Anna Rumshisky
A systematic overview of parameter-efficient fine-tuning methods, covering over 50 papers from 2019 to 2024.
0Jan 1, 2023Fine TuningScaling Laws
arXiv
Preprint2023
On the effectiveness of parameter-efficient fine-tuning
Unknown
This paper categorizes and analyzes parameter-efficient fine-tuning methods to understand their effectiveness.
0Jan 1, 2023Fine Tuning
Preprint2024
Parameter-efficient fine-tuning for large models: A comprehensive survey
Zeyu Han, Chao Gao, Jinyang Liu, et al.
This survey comprehensively reviews parameter-efficient fine-tuning (PEFT) methods for large models, categorizing approaches and analyzing their trade-offs.
0Mar 1, 2024Fine Tuning
arXiv